{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('depth_ndarray-1','rb') as f:\n",
    "    arr=pickle.load(f)\n",
    "arr>>=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0 in arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 511, numpy.ndarray, (480, 640), dtype('uint16'))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.min(),arr.max(),type(arr),arr.shape,arr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 255)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.uint16(668).astype(np.uint8),np.uint16(2047).astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1150a9048>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAD8CAYAAADzEfagAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e2e208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11597b0b8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAD8CAYAAADzEfagAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noYMH/j+dpmLALkTfvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113701c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth = arr.astype(np.uint8)\n",
    "plt.imshow(depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 255, numpy.ndarray, (480, 640), dtype('uint8'))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth.min(),depth.max(),type(depth),depth.shape,depth.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0 in depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newarr=arr.copy()\n",
    "for i in range(arr.shape[0]):\n",
    "    for j in range(arr.shape[1]):\n",
    "        newarr[i,j]=arr[i,j].astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10aaa1940>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAD8CAYAAADzEfagAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noYMH/j+dpmLALkTfvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a649518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(newarr)#还是那样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>630</th>\n",
       "      <th>631</th>\n",
       "      <th>632</th>\n",
       "      <th>633</th>\n",
       "      <th>634</th>\n",
       "      <th>635</th>\n",
       "      <th>636</th>\n",
       "      <th>637</th>\n",
       "      <th>638</th>\n",
       "      <th>639</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>926.220833</td>\n",
       "      <td>908.918750</td>\n",
       "      <td>908.589583</td>\n",
       "      <td>907.960417</td>\n",
       "      <td>904.677083</td>\n",
       "      <td>907.231250</td>\n",
       "      <td>909.408333</td>\n",
       "      <td>894.495833</td>\n",
       "      <td>894.162500</td>\n",
       "      <td>889.883333</td>\n",
       "      <td>...</td>\n",
       "      <td>816.914583</td>\n",
       "      <td>837.395833</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>353.636039</td>\n",
       "      <td>331.467893</td>\n",
       "      <td>331.604123</td>\n",
       "      <td>331.865318</td>\n",
       "      <td>328.076487</td>\n",
       "      <td>332.157907</td>\n",
       "      <td>336.341424</td>\n",
       "      <td>316.401591</td>\n",
       "      <td>316.491311</td>\n",
       "      <td>312.746789</td>\n",
       "      <td>...</td>\n",
       "      <td>117.648636</td>\n",
       "      <td>196.648180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>670.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>669.000000</td>\n",
       "      <td>669.000000</td>\n",
       "      <td>669.000000</td>\n",
       "      <td>669.000000</td>\n",
       "      <td>669.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>669.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>697.000000</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>797.000000</td>\n",
       "      <td>797.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>795.750000</td>\n",
       "      <td>794.000000</td>\n",
       "      <td>794.000000</td>\n",
       "      <td>793.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>794.000000</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>825.000000</td>\n",
       "      <td>822.000000</td>\n",
       "      <td>822.000000</td>\n",
       "      <td>821.000000</td>\n",
       "      <td>821.000000</td>\n",
       "      <td>821.000000</td>\n",
       "      <td>821.000000</td>\n",
       "      <td>819.000000</td>\n",
       "      <td>818.000000</td>\n",
       "      <td>818.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>850.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>847.250000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>845.000000</td>\n",
       "      <td>845.000000</td>\n",
       "      <td>845.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>831.250000</td>\n",
       "      <td>832.000000</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>2047.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 640 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0            1            2            3            4    \\\n",
       "count   480.000000   480.000000   480.000000   480.000000   480.000000   \n",
       "mean    926.220833   908.918750   908.589583   907.960417   904.677083   \n",
       "std     353.636039   331.467893   331.604123   331.865318   328.076487   \n",
       "min     670.000000   670.000000   669.000000   669.000000   669.000000   \n",
       "25%     797.000000   797.000000   796.000000   796.000000   795.000000   \n",
       "50%     825.000000   822.000000   822.000000   821.000000   821.000000   \n",
       "75%     850.000000   848.000000   848.000000   848.000000   848.000000   \n",
       "max    2047.000000  2047.000000  2047.000000  2047.000000  2047.000000   \n",
       "\n",
       "               5            6            7            8            9    \\\n",
       "count   480.000000   480.000000   480.000000   480.000000   480.000000   \n",
       "mean    907.231250   909.408333   894.495833   894.162500   889.883333   \n",
       "std     332.157907   336.341424   316.401591   316.491311   312.746789   \n",
       "min     669.000000   669.000000   670.000000   668.000000   669.000000   \n",
       "25%     795.000000   795.750000   794.000000   794.000000   793.000000   \n",
       "50%     821.000000   821.000000   819.000000   818.000000   818.000000   \n",
       "75%     847.250000   848.000000   845.000000   845.000000   845.000000   \n",
       "max    2047.000000  2047.000000  2047.000000  2047.000000  2047.000000   \n",
       "\n",
       "        ...            630          631     632     633     634     635  \\\n",
       "count   ...     480.000000   480.000000   480.0   480.0   480.0   480.0   \n",
       "mean    ...     816.914583   837.395833  2047.0  2047.0  2047.0  2047.0   \n",
       "std     ...     117.648636   196.648180     0.0     0.0     0.0     0.0   \n",
       "min     ...     698.000000   697.000000  2047.0  2047.0  2047.0  2047.0   \n",
       "25%     ...     795.000000   794.000000  2047.0  2047.0  2047.0  2047.0   \n",
       "50%     ...     808.000000   808.000000  2047.0  2047.0  2047.0  2047.0   \n",
       "75%     ...     831.250000   832.000000  2047.0  2047.0  2047.0  2047.0   \n",
       "max     ...    2047.000000  2047.000000  2047.0  2047.0  2047.0  2047.0   \n",
       "\n",
       "          636     637     638     639  \n",
       "count   480.0   480.0   480.0   480.0  \n",
       "mean   2047.0  2047.0  2047.0  2047.0  \n",
       "std       0.0     0.0     0.0     0.0  \n",
       "min    2047.0  2047.0  2047.0  2047.0  \n",
       "25%    2047.0  2047.0  2047.0  2047.0  \n",
       "50%    2047.0  2047.0  2047.0  2047.0  \n",
       "75%    2047.0  2047.0  2047.0  2047.0  \n",
       "max    2047.0  2047.0  2047.0  2047.0  \n",
       "\n",
       "[8 rows x 640 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(arr)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr.all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#保存为图片\n",
    "cv2.imwrite('depth_ndarray-1.jpg',arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](depth_ndarray-1.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('depth_ndarray-depth.jpg',depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](depth_ndarray-depth.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#是一样的\n",
    "!diff depth_ndarray-depth.jpg depth_ndarray-1.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "depthr=np.empty([depth.shape[0],depth.shape[1],3], dtype=np.uint8)\n",
    "for i in range(depth.shape[0]):\n",
    "    for j in range(depth.shape[1]):\n",
    "        depthr[i,j]=[0,depth[i,j],0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10a6c30b8>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAD8CAYAAADzEfagAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a6fa9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(depthr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480, 640, 3)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth.shape[0],depth.shape[1],3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('depth_ndarray-depthr.jpg',depthr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](depth_ndarray-depthr.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "depth2=depth.copy()\n",
    "for x in range(depth2.min()+1,depth2.max(),2):\n",
    "    depth2[depth2==x]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x115aed5c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAD8CAYAAADzEfagAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neej/AxyygFJs4v6qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115941438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(depth2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.imsave('depth_plt-0.jpg',depth)\n",
    "plt.imsave('depth_plt-1.jpg',depth2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('depth_ndarray-wave.jpg',depth2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 255)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth2.min(),depth2.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "array2=arr.copy()\n",
    "for x in range(arr.min()+1,arr.max(),2):\n",
    "    array2[array2==x]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'array2' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-86-d80178bc19a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'array2' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}